Overview

Dataset statistics

Number of variables19
Number of observations679
Missing cells2948
Missing cells (%)22.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory100.9 KiB
Average record size in memory152.2 B

Variable types

Numeric13
DateTime1
Categorical5

Warnings

Channel has constant value "Reclame Aqui" Constant
Tickets has constant value "1" Constant
Requester email has a high cardinality: 679 distinct values High cardinality
df_index is highly correlated with Requester external ID and 1 other fieldsHigh correlation
Requester external ID is highly correlated with df_index and 1 other fieldsHigh correlation
Requester_ID is highly correlated with df_index and 1 other fieldsHigh correlation
Volume_CHAT is highly correlated with Volume_SOCIAL and 2 other fieldsHigh correlation
Volume_SOCIAL is highly correlated with Volume_CHAT and 3 other fieldsHigh correlation
Tempo_Medio_Chat is highly correlated with Volume_SOCIAL and 1 other fieldsHigh correlation
Tempo_Medio_Social is highly correlated with Volume_CHAT and 2 other fieldsHigh correlation
AWT_Chat is highly correlated with Volume_SOCIAL and 1 other fieldsHigh correlation
CSAT_Rated is highly correlated with Volume_CHAT and 1 other fieldsHigh correlation
df_index is highly correlated with Requester external ID and 1 other fieldsHigh correlation
Requester external ID is highly correlated with df_index and 1 other fieldsHigh correlation
Requester_ID is highly correlated with df_index and 1 other fieldsHigh correlation
Volume_CHAT is highly correlated with Volume_SOCIAL and 2 other fieldsHigh correlation
Volume_SOCIAL is highly correlated with Volume_CHAT and 3 other fieldsHigh correlation
Tempo_Medio_Chat is highly correlated with Volume_SOCIAL and 1 other fieldsHigh correlation
Tempo_Medio_Social is highly correlated with Volume_CHAT and 3 other fieldsHigh correlation
AWT_Chat is highly correlated with Volume_SOCIAL and 1 other fieldsHigh correlation
%NFCR is highly correlated with Volume_CHAT and 1 other fieldsHigh correlation
%Insatisfação(CSAT) is highly correlated with CSAT_RatedHigh correlation
CSAT_Rated is highly correlated with Volume_SOCIAL and 1 other fieldsHigh correlation
df_index is highly correlated with Requester external ID and 1 other fieldsHigh correlation
Requester external ID is highly correlated with df_index and 1 other fieldsHigh correlation
Requester_ID is highly correlated with df_index and 1 other fieldsHigh correlation
Volume_CHAT is highly correlated with Volume_SOCIAL and 1 other fieldsHigh correlation
Volume_SOCIAL is highly correlated with Volume_CHAT and 3 other fieldsHigh correlation
Tempo_Medio_Chat is highly correlated with Volume_SOCIAL and 1 other fieldsHigh correlation
Tempo_Medio_Social is highly correlated with Volume_CHAT and 3 other fieldsHigh correlation
AWT_Chat is highly correlated with Volume_SOCIAL and 1 other fieldsHigh correlation
%NFCR is highly correlated with Tempo_Medio_SocialHigh correlation
%Insatisfação(CSAT) is highly correlated with CSAT_RatedHigh correlation
CSAT_Rated is highly correlated with Volume_SOCIAL and 1 other fieldsHigh correlation
Volume_SOCIAL is highly correlated with Requester_ID and 4 other fieldsHigh correlation
Requester_ID is highly correlated with Volume_SOCIAL and 3 other fieldsHigh correlation
%Insatisfação(CSAT) is highly correlated with CSAT_Rated and 1 other fieldsHigh correlation
Ticket ID is highly correlated with Volume_SOCIAL and 2 other fieldsHigh correlation
%NFCR is highly correlated with Volume_EMAIL and 2 other fieldsHigh correlation
Tempo_Medio_Social is highly correlated with Requester_ID and 4 other fieldsHigh correlation
Volume_EMAIL is highly correlated with %NFCRHigh correlation
Requester external ID is highly correlated with Volume_SOCIAL and 3 other fieldsHigh correlation
CSAT_Rated is highly correlated with %Insatisfação(CSAT) and 1 other fieldsHigh correlation
Volume_CHAT is highly correlated with %Insatisfação(CSAT) and 2 other fieldsHigh correlation
Assignee email is highly correlated with Volume_SOCIAL and 4 other fieldsHigh correlation
Tempo_Medio_Email is highly correlated with Assignee emailHigh correlation
df_index is highly correlated with Volume_SOCIAL and 3 other fieldsHigh correlation
Volume_SOCIAL is highly correlated with Channel and 2 other fieldsHigh correlation
Channel is highly correlated with Volume_SOCIAL and 2 other fieldsHigh correlation
Tickets is highly correlated with Volume_SOCIAL and 2 other fieldsHigh correlation
Assignee email is highly correlated with Volume_SOCIAL and 2 other fieldsHigh correlation
Volume_CHAT has 261 (38.4%) missing values Missing
Volume_EMAIL has 408 (60.1%) missing values Missing
Volume_SOCIAL has 672 (99.0%) missing values Missing
Tempo_Medio_Chat has 264 (38.9%) missing values Missing
Tempo_Medio_Email has 409 (60.2%) missing values Missing
Tempo_Medio_Social has 672 (99.0%) missing values Missing
AWT_Chat has 262 (38.6%) missing values Missing
Requester email is uniformly distributed Uniform
df_index has unique values Unique
Requester external ID has unique values Unique
Ticket ID has unique values Unique
Requester email has unique values Unique
Requester_ID has unique values Unique
%NFCR has 427 (62.9%) zeros Zeros
%Insatisfação(CSAT) has 562 (82.8%) zeros Zeros
CSAT_Rated has 429 (63.2%) zeros Zeros

Reproduction

Analysis started2021-06-15 16:41:28.822579
Analysis finished2021-06-15 16:42:01.187303
Duration32.36 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct679
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean463.3460972
Minimum0
Maximum932
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2021-06-15T13:42:01.321641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43.9
Q1239.5
median455
Q3692.5
95-th percentile886.1
Maximum932
Range932
Interquartile range (IQR)453

Descriptive statistics

Standard deviation266.857943
Coefficient of variation (CV)0.5759365291
Kurtosis-1.156358872
Mean463.3460972
Median Absolute Deviation (MAD)230
Skewness0.03692166865
Sum314612
Variance71213.16175
MonotonicityStrictly increasing
2021-06-15T13:42:01.474233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9321
 
0.1%
3081
 
0.1%
3211
 
0.1%
3191
 
0.1%
3181
 
0.1%
3171
 
0.1%
3161
 
0.1%
3141
 
0.1%
3111
 
0.1%
3101
 
0.1%
Other values (669)669
98.5%
ValueCountFrequency (%)
01
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
71
0.1%
91
0.1%
101
0.1%
111
0.1%
121
0.1%
ValueCountFrequency (%)
9321
0.1%
9301
0.1%
9291
0.1%
9281
0.1%
9271
0.1%
9261
0.1%
9241
0.1%
9231
0.1%
9221
0.1%
9201
0.1%

Requester external ID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct679
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8347895.549
Minimum21593
Maximum19066870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2021-06-15T13:42:01.638817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum21593
5-th percentile453050.4
Q13568784
median7780143
Q313384518.5
95-th percentile17118987.9
Maximum19066870
Range19045277
Interquartile range (IQR)9815734.5

Descriptive statistics

Standard deviation5521532.082
Coefficient of variation (CV)0.6614280269
Kurtosis-1.183382488
Mean8347895.549
Median Absolute Deviation (MAD)4579825
Skewness0.1897955962
Sum5668221078
Variance3.048731653 × 1013
MonotonicityStrictly increasing
2021-06-15T13:42:01.808678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88637331
 
0.1%
50899731
 
0.1%
79408671
 
0.1%
81405321
 
0.1%
185251461
 
0.1%
105405091
 
0.1%
9311641
 
0.1%
170504261
 
0.1%
61795231
 
0.1%
142268111
 
0.1%
Other values (669)669
98.5%
ValueCountFrequency (%)
215931
0.1%
300841
0.1%
470271
0.1%
541941
0.1%
562341
0.1%
573501
0.1%
580581
0.1%
582461
0.1%
674491
0.1%
810431
0.1%
ValueCountFrequency (%)
190668701
0.1%
189340001
0.1%
188687951
0.1%
188453441
0.1%
188381441
0.1%
188144671
0.1%
186857711
0.1%
186851621
0.1%
186559611
0.1%
186402651
0.1%
Distinct113
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
Minimum2021-01-04 00:00:00
Maximum2021-06-10 00:00:00
2021-06-15T13:42:01.987289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:42:02.154849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Channel
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
Reclame Aqui
679 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters8148
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReclame Aqui
2nd rowReclame Aqui
3rd rowReclame Aqui
4th rowReclame Aqui
5th rowReclame Aqui

Common Values

ValueCountFrequency (%)
Reclame Aqui679
100.0%

Length

2021-06-15T13:42:02.418137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-15T13:42:02.494932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
aqui679
50.0%
reclame679
50.0%

Most occurring characters

ValueCountFrequency (%)
e1358
16.7%
R679
8.3%
c679
8.3%
l679
8.3%
a679
8.3%
m679
8.3%
679
8.3%
A679
8.3%
q679
8.3%
u679
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6111
75.0%
Uppercase Letter1358
 
16.7%
Space Separator679
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1358
22.2%
c679
11.1%
l679
11.1%
a679
11.1%
m679
11.1%
q679
11.1%
u679
11.1%
i679
11.1%
Uppercase Letter
ValueCountFrequency (%)
R679
50.0%
A679
50.0%
Space Separator
ValueCountFrequency (%)
679
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7469
91.7%
Common679
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1358
18.2%
R679
9.1%
c679
9.1%
l679
9.1%
a679
9.1%
m679
9.1%
A679
9.1%
q679
9.1%
u679
9.1%
i679
9.1%
Common
ValueCountFrequency (%)
679
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1358
16.7%
R679
8.3%
c679
8.3%
l679
8.3%
a679
8.3%
m679
8.3%
679
8.3%
A679
8.3%
q679
8.3%
u679
8.3%

Ticket ID
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct679
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3765880.471
Minimum3459738
Maximum4080394
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2021-06-15T13:42:02.572726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3459738
5-th percentile3521072.3
Q13615320.5
median3757266
Q33922106.5
95-th percentile4017061.3
Maximum4080394
Range620656
Interquartile range (IQR)306786

Descriptive statistics

Standard deviation169678.3309
Coefficient of variation (CV)0.04505674893
Kurtosis-1.351913809
Mean3765880.471
Median Absolute Deviation (MAD)153313
Skewness0.03236582552
Sum2557032840
Variance2.879073597 × 1010
MonotonicityNot monotonic
2021-06-15T13:42:02.707333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37069361
 
0.1%
39468431
 
0.1%
38178311
 
0.1%
35167741
 
0.1%
38661551
 
0.1%
36345321
 
0.1%
34962911
 
0.1%
38301131
 
0.1%
38557001
 
0.1%
39515491
 
0.1%
Other values (669)669
98.5%
ValueCountFrequency (%)
34597381
0.1%
34624331
0.1%
34735301
0.1%
34738501
0.1%
34744271
0.1%
34746591
0.1%
34852981
0.1%
34894641
0.1%
34895021
0.1%
34923981
0.1%
ValueCountFrequency (%)
40803941
0.1%
40784171
0.1%
40780471
0.1%
40715841
0.1%
40713511
0.1%
40696211
0.1%
40625491
0.1%
40623411
0.1%
40584091
0.1%
40583031
0.1%

Assignee email
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
kaique.barbosa@gympass.com
160 
suellen.franco+core@gympass.com
121 
danielle.hernandes@gympass.com
117 
luciana.melo@gympass.com
79 
barbara.priscila@gympass.com
67 
Other values (20)
135 

Length

Max length31
Median length27
Mean length27.70397644
Min length23

Characters and Unicode

Total characters18811
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)1.6%

Sample

1st rowkaique.barbosa@gympass.com
2nd rowluciana.melo@gympass.com
3rd rowanderson.santos@gympass.com
4th rowgabriel.almeida@gympass.com
5th rowluciana.melo@gympass.com

Common Values

ValueCountFrequency (%)
kaique.barbosa@gympass.com160
23.6%
suellen.franco+core@gympass.com121
17.8%
danielle.hernandes@gympass.com117
17.2%
luciana.melo@gympass.com79
11.6%
barbara.priscila@gympass.com67
9.9%
anderson.santos@gympass.com60
 
8.8%
gabriel.almeida@gympass.com30
 
4.4%
elizabete.damiao@gympass.com14
 
2.1%
caroline.roberto@gympass.com5
 
0.7%
aline.colombo@gympass.com5
 
0.7%
Other values (15)21
 
3.1%

Length

2021-06-15T13:42:03.023518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kaique.barbosa@gympass.com160
23.6%
suellen.franco+core@gympass.com121
17.8%
danielle.hernandes@gympass.com117
17.2%
luciana.melo@gympass.com79
11.6%
barbara.priscila@gympass.com67
9.9%
anderson.santos@gympass.com60
 
8.8%
gabriel.almeida@gympass.com30
 
4.4%
elizabete.damiao@gympass.com14
 
2.1%
caroline.roberto@gympass.com5
 
0.7%
aline.colombo@gympass.com5
 
0.7%
Other values (15)21
 
3.1%

Most occurring characters

ValueCountFrequency (%)
a2245
11.9%
s2024
 
10.8%
m1493
 
7.9%
.1357
 
7.2%
o1347
 
7.2%
e1271
 
6.8%
c1088
 
5.8%
n883
 
4.7%
r847
 
4.5%
l802
 
4.3%
Other values (19)5454
29.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16651
88.5%
Other Punctuation2036
 
10.8%
Math Symbol121
 
0.6%
Connector Punctuation2
 
< 0.1%
Decimal Number1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2245
13.5%
s2024
12.2%
m1493
 
9.0%
o1347
 
8.1%
e1271
 
7.6%
c1088
 
6.5%
n883
 
5.3%
r847
 
5.1%
l802
 
4.8%
p747
 
4.5%
Other values (14)3904
23.4%
Other Punctuation
ValueCountFrequency (%)
.1357
66.7%
@679
33.3%
Math Symbol
ValueCountFrequency (%)
+121
100.0%
Connector Punctuation
ValueCountFrequency (%)
_2
100.0%
Decimal Number
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16651
88.5%
Common2160
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2245
13.5%
s2024
12.2%
m1493
 
9.0%
o1347
 
8.1%
e1271
 
7.6%
c1088
 
6.5%
n883
 
5.3%
r847
 
5.1%
l802
 
4.8%
p747
 
4.5%
Other values (14)3904
23.4%
Common
ValueCountFrequency (%)
.1357
62.8%
@679
31.4%
+121
 
5.6%
_2
 
0.1%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII18811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2245
11.9%
s2024
 
10.8%
m1493
 
7.9%
.1357
 
7.2%
o1347
 
7.2%
e1271
 
6.8%
c1088
 
5.8%
n883
 
4.7%
r847
 
4.5%
l802
 
4.3%
Other values (19)5454
29.0%

Requester email
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct679
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
rafaela.almeida@facc.ufjf.br
 
1
gustavo.hocarvalho@hsl.org.br
 
1
rpereira.png@gmail.com
 
1
ka.erika2@gmail.com
 
1
nolive22@ford.com
 
1
Other values (674)
674 

Length

Max length67
Median length25
Mean length25.04565538
Min length12

Characters and Unicode

Total characters17006
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique679 ?
Unique (%)100.0%

Sample

1st rowjoabinho99@hotmail.com
2nd rowcamila.almeida@unilever.com
3rd rowflashlav@gmail.com
4th rownancy.jikihara@voith.com
5th rowerikabazilio19@gmail.com

Common Values

ValueCountFrequency (%)
rafaela.almeida@facc.ufjf.br1
 
0.1%
gustavo.hocarvalho@hsl.org.br1
 
0.1%
rpereira.png@gmail.com1
 
0.1%
ka.erika2@gmail.com1
 
0.1%
nolive22@ford.com1
 
0.1%
nilsonmaesta@gmail.com1
 
0.1%
mirna.folco@fundacaorenova.org1
 
0.1%
alves3392@hotmail.com1
 
0.1%
guilhermemoura403@gmail.com1
 
0.1%
talita.esperandio@localiza.com1
 
0.1%
Other values (669)669
98.5%

Length

2021-06-15T13:42:03.321869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rafaela.almeida@facc.ufjf.br1
 
0.1%
gustavo.hocarvalho@hsl.org.br1
 
0.1%
rpereira.png@gmail.com1
 
0.1%
ka.erika2@gmail.com1
 
0.1%
nolive22@ford.com1
 
0.1%
nilsonmaesta@gmail.com1
 
0.1%
mirna.folco@fundacaorenova.org1
 
0.1%
alves3392@hotmail.com1
 
0.1%
guilhermemoura403@gmail.com1
 
0.1%
talita.esperandio@localiza.com1
 
0.1%
Other values (669)669
98.5%

Most occurring characters

ValueCountFrequency (%)
a1904
 
11.2%
o1668
 
9.8%
m1363
 
8.0%
i1257
 
7.4%
.1126
 
6.6%
l1061
 
6.2%
c1028
 
6.0%
r913
 
5.4%
e868
 
5.1%
@679
 
4.0%
Other values (30)5139
30.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14636
86.1%
Other Punctuation1805
 
10.6%
Decimal Number457
 
2.7%
Connector Punctuation62
 
0.4%
Dash Punctuation46
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1904
13.0%
o1668
11.4%
m1363
9.3%
i1257
 
8.6%
l1061
 
7.2%
c1028
 
7.0%
r913
 
6.2%
e868
 
5.9%
s639
 
4.4%
n610
 
4.2%
Other values (16)3325
22.7%
Decimal Number
ValueCountFrequency (%)
179
17.3%
067
14.7%
261
13.3%
948
10.5%
841
9.0%
338
8.3%
434
7.4%
533
7.2%
629
 
6.3%
727
 
5.9%
Other Punctuation
ValueCountFrequency (%)
.1126
62.4%
@679
37.6%
Connector Punctuation
ValueCountFrequency (%)
_62
100.0%
Dash Punctuation
ValueCountFrequency (%)
-46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14636
86.1%
Common2370
 
13.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1904
13.0%
o1668
11.4%
m1363
9.3%
i1257
 
8.6%
l1061
 
7.2%
c1028
 
7.0%
r913
 
6.2%
e868
 
5.9%
s639
 
4.4%
n610
 
4.2%
Other values (16)3325
22.7%
Common
ValueCountFrequency (%)
.1126
47.5%
@679
28.6%
179
 
3.3%
067
 
2.8%
_62
 
2.6%
261
 
2.6%
948
 
2.0%
-46
 
1.9%
841
 
1.7%
338
 
1.6%
Other values (4)123
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII17006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1904
 
11.2%
o1668
 
9.8%
m1363
 
8.0%
i1257
 
7.4%
.1126
 
6.6%
l1061
 
6.2%
c1028
 
6.0%
r913
 
5.4%
e868
 
5.1%
@679
 
4.0%
Other values (30)5139
30.2%

Tickets
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
1
679 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters679
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1679
100.0%

Length

2021-06-15T13:42:03.559675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-15T13:42:03.635615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1679
100.0%

Most occurring characters

ValueCountFrequency (%)
1679
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number679
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1679
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common679
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1679
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII679
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1679
100.0%

Requester_ID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct679
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8347895.549
Minimum21593
Maximum19066870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2021-06-15T13:42:03.720421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum21593
5-th percentile453050.4
Q13568784
median7780143
Q313384518.5
95-th percentile17118987.9
Maximum19066870
Range19045277
Interquartile range (IQR)9815734.5

Descriptive statistics

Standard deviation5521532.082
Coefficient of variation (CV)0.6614280269
Kurtosis-1.183382488
Mean8347895.549
Median Absolute Deviation (MAD)4579825
Skewness0.1897955962
Sum5668221078
Variance3.048731653 × 1013
MonotonicityStrictly increasing
2021-06-15T13:42:04.055487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83661641
 
0.1%
52913461
 
0.1%
101143981
 
0.1%
137740181
 
0.1%
9311641
 
0.1%
12165501
 
0.1%
78421371
 
0.1%
33508691
 
0.1%
125271461
 
0.1%
91395391
 
0.1%
Other values (669)669
98.5%
ValueCountFrequency (%)
215931
0.1%
300841
0.1%
470271
0.1%
541941
0.1%
562341
0.1%
573501
0.1%
580581
0.1%
582461
0.1%
674491
0.1%
810431
0.1%
ValueCountFrequency (%)
190668701
0.1%
189340001
0.1%
188687951
0.1%
188453441
0.1%
188381441
0.1%
188144671
0.1%
186857711
0.1%
186851621
0.1%
186559611
0.1%
186402651
0.1%

Volume_CHAT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct12
Distinct (%)2.9%
Missing261
Missing (%)38.4%
Infinite0
Infinite (%)0.0%
Mean2.294258373
Minimum0
Maximum16
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2021-06-15T13:42:04.190130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum16
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.919736081
Coefficient of variation (CV)0.8367567066
Kurtosis11.45467045
Mean2.294258373
Median Absolute Deviation (MAD)1
Skewness2.668019466
Sum959
Variance3.685386619
MonotonicityNot monotonic
2021-06-15T13:42:04.298228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1203
29.9%
276
 
11.2%
362
 
9.1%
432
 
4.7%
521
 
3.1%
76
 
0.9%
66
 
0.9%
85
 
0.7%
94
 
0.6%
151
 
0.1%
Other values (2)2
 
0.3%
(Missing)261
38.4%
ValueCountFrequency (%)
01
 
0.1%
1203
29.9%
276
 
11.2%
362
 
9.1%
432
 
4.7%
521
 
3.1%
66
 
0.9%
76
 
0.9%
85
 
0.7%
94
 
0.6%
ValueCountFrequency (%)
161
 
0.1%
151
 
0.1%
94
 
0.6%
85
 
0.7%
76
 
0.9%
66
 
0.9%
521
 
3.1%
432
4.7%
362
9.1%
276
11.2%

Volume_EMAIL
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)3.0%
Missing408
Missing (%)60.1%
Infinite0
Infinite (%)0.0%
Mean1.586715867
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2021-06-15T13:42:04.421897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3.5
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.185892658
Coefficient of variation (CV)0.7473881636
Kurtosis21.35105228
Mean1.586715867
Median Absolute Deviation (MAD)0
Skewness3.879229103
Sum430
Variance1.406341397
MonotonicityNot monotonic
2021-06-15T13:42:04.515647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1179
26.4%
259
 
8.7%
319
 
2.8%
48
 
1.2%
52
 
0.3%
82
 
0.3%
71
 
0.1%
111
 
0.1%
(Missing)408
60.1%
ValueCountFrequency (%)
1179
26.4%
259
 
8.7%
319
 
2.8%
48
 
1.2%
52
 
0.3%
71
 
0.1%
82
 
0.3%
111
 
0.1%
ValueCountFrequency (%)
111
 
0.1%
82
 
0.3%
71
 
0.1%
52
 
0.3%
48
 
1.2%
319
 
2.8%
259
 
8.7%
1179
26.4%

Volume_SOCIAL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)42.9%
Missing672
Missing (%)99.0%
Memory size5.4 KiB
1.0
3.0
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)14.3%

Sample

1st row1.0
2nd row3.0
3rd row3.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.04
 
0.6%
3.02
 
0.3%
2.01
 
0.1%
(Missing)672
99.0%

Length

2021-06-15T13:42:04.747170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-15T13:42:04.825959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.04
57.1%
3.02
28.6%
2.01
 
14.3%

Most occurring characters

ValueCountFrequency (%)
.7
33.3%
07
33.3%
14
19.0%
32
 
9.5%
21
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number14
66.7%
Other Punctuation7
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07
50.0%
14
28.6%
32
 
14.3%
21
 
7.1%
Other Punctuation
ValueCountFrequency (%)
.7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common21
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.7
33.3%
07
33.3%
14
19.0%
32
 
9.5%
21
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII21
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.7
33.3%
07
33.3%
14
19.0%
32
 
9.5%
21
 
4.8%

Tempo_Medio_Chat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct376
Distinct (%)90.6%
Missing264
Missing (%)38.9%
Infinite0
Infinite (%)0.0%
Mean1496.860241
Minimum15
Maximum6115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2021-06-15T13:42:04.930071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile451.9
Q1923
median1324
Q31874
95-th percentile3055.8
Maximum6115
Range6100
Interquartile range (IQR)951

Descriptive statistics

Standard deviation848.0170446
Coefficient of variation (CV)0.5665305427
Kurtosis3.679638398
Mean1496.860241
Median Absolute Deviation (MAD)446
Skewness1.51206775
Sum621197
Variance719132.908
MonotonicityNot monotonic
2021-06-15T13:42:05.066682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11003
 
0.4%
15923
 
0.4%
4153
 
0.4%
8572
 
0.3%
14552
 
0.3%
17232
 
0.3%
27692
 
0.3%
19592
 
0.3%
14072
 
0.3%
14092
 
0.3%
Other values (366)392
57.7%
(Missing)264
38.9%
ValueCountFrequency (%)
151
0.1%
571
0.1%
781
0.1%
2441
0.1%
2631
0.1%
2781
0.1%
3221
0.1%
3541
0.1%
3771
0.1%
3821
0.1%
ValueCountFrequency (%)
61151
0.1%
52711
0.1%
48151
0.1%
43901
0.1%
42491
0.1%
41081
0.1%
40131
0.1%
39701
0.1%
39581
0.1%
38411
0.1%

Tempo_Medio_Email
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct266
Distinct (%)98.5%
Missing409
Missing (%)60.2%
Infinite0
Infinite (%)0.0%
Mean12222.52222
Minimum1470
Maximum134314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2021-06-15T13:42:05.208302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1470
5-th percentile3069.1
Q15652.75
median8976.5
Q315106
95-th percentile29221.9
Maximum134314
Range132844
Interquartile range (IQR)9453.25

Descriptive statistics

Standard deviation12276.98074
Coefficient of variation (CV)1.004455587
Kurtosis40.68030281
Mean12222.52222
Median Absolute Deviation (MAD)4363.5
Skewness5.088451653
Sum3300081
Variance150724256.1
MonotonicityNot monotonic
2021-06-15T13:42:05.357871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78922
 
0.3%
68912
 
0.3%
42622
 
0.3%
158432
 
0.3%
32961
 
0.1%
54581
 
0.1%
223961
 
0.1%
210261
 
0.1%
104581
 
0.1%
181021
 
0.1%
Other values (256)256
37.7%
(Missing)409
60.2%
ValueCountFrequency (%)
14701
0.1%
16341
0.1%
19321
0.1%
21111
0.1%
21721
0.1%
21831
0.1%
22441
0.1%
25251
0.1%
25691
0.1%
26401
0.1%
ValueCountFrequency (%)
1343141
0.1%
809641
0.1%
659031
0.1%
578651
0.1%
433971
0.1%
387051
0.1%
385201
0.1%
347281
0.1%
329631
0.1%
327241
0.1%

Tempo_Medio_Social
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7
Distinct (%)100.0%
Missing672
Missing (%)99.0%
Infinite0
Infinite (%)0.0%
Mean15062.14286
Minimum5197
Maximum40679
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2021-06-15T13:42:05.485561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5197
5-th percentile5217.1
Q15633
median7483
Q320405
95-th percentile35264.6
Maximum40679
Range35482
Interquartile range (IQR)14772

Descriptive statistics

Standard deviation13267.00173
Coefficient of variation (CV)0.8808176799
Kurtosis1.570188707
Mean15062.14286
Median Absolute Deviation (MAD)2286
Skewness1.418327471
Sum105435
Variance176013334.8
MonotonicityNot monotonic
2021-06-15T13:42:05.584265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
51971
 
0.1%
406791
 
0.1%
74831
 
0.1%
52641
 
0.1%
60021
 
0.1%
226311
 
0.1%
181791
 
0.1%
(Missing)672
99.0%
ValueCountFrequency (%)
51971
0.1%
52641
0.1%
60021
0.1%
74831
0.1%
181791
0.1%
226311
0.1%
406791
0.1%
ValueCountFrequency (%)
406791
0.1%
226311
0.1%
181791
0.1%
74831
0.1%
60021
0.1%
52641
0.1%
51971
0.1%

AWT_Chat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct178
Distinct (%)42.7%
Missing262
Missing (%)38.6%
Infinite0
Infinite (%)0.0%
Mean144.8009592
Minimum5
Maximum1981
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2021-06-15T13:42:05.724890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q117
median35
Q3109
95-th percentile772.2
Maximum1981
Range1976
Interquartile range (IQR)92

Descriptive statistics

Standard deviation286.7212634
Coefficient of variation (CV)1.980106105
Kurtosis15.08869612
Mean144.8009592
Median Absolute Deviation (MAD)24
Skewness3.604342587
Sum60382
Variance82209.08288
MonotonicityNot monotonic
2021-06-15T13:42:05.859530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
814
 
2.1%
1713
 
1.9%
1112
 
1.8%
1911
 
1.6%
1310
 
1.5%
910
 
1.5%
129
 
1.3%
249
 
1.3%
79
 
1.3%
148
 
1.2%
Other values (168)312
45.9%
(Missing)262
38.6%
ValueCountFrequency (%)
52
 
0.3%
67
1.0%
79
1.3%
814
2.1%
910
1.5%
107
1.0%
1112
1.8%
129
1.3%
1310
1.5%
148
1.2%
ValueCountFrequency (%)
19811
0.1%
18102
0.3%
17501
0.1%
16911
0.1%
14791
0.1%
12081
0.1%
11911
0.1%
10111
0.1%
9971
0.1%
9681
0.1%

%NFCR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1935250076
Minimum0
Maximum1
Zeros427
Zeros (%)62.9%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2021-06-15T13:42:05.998189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.4
95-th percentile0.75
Maximum1
Range1
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.2878945825
Coefficient of variation (CV)1.48763504
Kurtosis0.6655898517
Mean0.1935250076
Median Absolute Deviation (MAD)0
Skewness1.301673725
Sum131.4034802
Variance0.08288329061
MonotonicityNot monotonic
2021-06-15T13:42:06.120902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0427
62.9%
0.582
 
12.1%
0.333333333340
 
5.9%
130
 
4.4%
0.666666666719
 
2.8%
0.2517
 
2.5%
0.214
 
2.1%
0.7512
 
1.8%
0.410
 
1.5%
0.64
 
0.6%
Other values (14)24
 
3.5%
ValueCountFrequency (%)
0427
62.9%
0.16666666673
 
0.4%
0.214
 
2.1%
0.2517
 
2.5%
0.32
 
0.3%
0.333333333340
 
5.9%
0.3752
 
0.3%
0.410
 
1.5%
0.42857142863
 
0.4%
0.44444444441
 
0.1%
ValueCountFrequency (%)
130
4.4%
0.82
 
0.3%
0.77777777781
 
0.1%
0.7512
 
1.8%
0.71428571431
 
0.1%
0.71
 
0.1%
0.666666666719
2.8%
0.64705882351
 
0.1%
0.63636363641
 
0.1%
0.64
 
0.6%

%Insatisfação(CSAT)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1507250481
Minimum0
Maximum1
Zeros562
Zeros (%)82.8%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2021-06-15T13:42:06.245698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3438570259
Coefficient of variation (CV)2.281352901
Kurtosis1.994993866
Mean0.1507250481
Median Absolute Deviation (MAD)0
Skewness1.956843913
Sum102.3423077
Variance0.1182376542
MonotonicityNot monotonic
2021-06-15T13:42:06.351419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0562
82.8%
189
 
13.1%
0.520
 
2.9%
0.33333333334
 
0.6%
0.69230769231
 
0.1%
0.41
 
0.1%
0.66666666671
 
0.1%
0.251
 
0.1%
ValueCountFrequency (%)
0562
82.8%
0.251
 
0.1%
0.33333333334
 
0.6%
0.41
 
0.1%
0.520
 
2.9%
0.66666666671
 
0.1%
0.69230769231
 
0.1%
189
 
13.1%
ValueCountFrequency (%)
189
 
13.1%
0.69230769231
 
0.1%
0.66666666671
 
0.1%
0.520
 
2.9%
0.41
 
0.1%
0.33333333334
 
0.6%
0.251
 
0.1%
0562
82.8%

CSAT_Rated
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5375552283
Minimum0
Maximum13
Zeros429
Zeros (%)63.2%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2021-06-15T13:42:06.450120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum13
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9676114356
Coefficient of variation (CV)1.800022369
Kurtosis44.21342647
Mean0.5375552283
Median Absolute Deviation (MAD)0
Skewness4.659882536
Sum365
Variance0.9362718904
MonotonicityNot monotonic
2021-06-15T13:42:06.560826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0429
63.2%
1175
25.8%
260
 
8.8%
36
 
0.9%
55
 
0.7%
42
 
0.3%
131
 
0.1%
61
 
0.1%
ValueCountFrequency (%)
0429
63.2%
1175
25.8%
260
 
8.8%
36
 
0.9%
42
 
0.3%
55
 
0.7%
61
 
0.1%
131
 
0.1%
ValueCountFrequency (%)
131
 
0.1%
61
 
0.1%
55
 
0.7%
42
 
0.3%
36
 
0.9%
260
 
8.8%
1175
25.8%
0429
63.2%

Interactions

2021-06-15T13:41:36.866301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:37.010915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:37.156524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:37.283184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:37.419655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:37.547790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:37.651513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:37.766237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:37.878937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:37.988642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:38.107325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:38.229005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:38.342695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:38.459352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:38.603998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:38.771123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:38.924712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:39.080295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:39.226904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:39.356558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:39.494191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:39.629819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:39.762472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:39.990830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:40.133728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:40.284837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:40.429454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:40.557113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:40.707706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:40.846337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:41.015888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:41.151488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:41.271346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:41.402994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:41.534643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:41.640358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:41.778235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:41.905896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:42.040568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:42.177204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:42.317794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:42.472420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:42.627077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:42.794970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:42.939615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:43.068846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:43.219442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:43.369041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:43.501686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:43.678183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:43.825862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:43.986849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:44.135676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:44.261341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:44.416927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:44.556836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:44.802149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:44.940876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:45.073522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:45.208165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:45.330802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:45.436544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:45.600297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:45.731527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:45.861181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:46.007758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:46.105495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:46.227215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:46.336888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:46.454731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:46.609357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:46.712584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:46.835754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:46.951694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:47.060404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:47.210843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:47.321547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:47.439232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:47.556174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:47.686572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:47.836172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:47.958877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:48.093622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:48.220284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:48.338935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:48.472577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:48.597244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:48.717952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:48.860570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:48.984486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:49.108156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:49.217582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:49.327093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:49.479953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:49.613636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:49.745624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:49.863905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:50.095808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:50.210921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:50.344448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:50.451242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:50.564107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:50.694510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:50.799398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:50.929258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:51.047379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:51.196565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:51.293649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:51.428007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:51.542879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:51.643083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:51.780522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:51.912374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:52.042562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:52.161035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:52.309618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:52.422341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:52.585791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:52.710528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:52.879356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:53.026475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:53.178686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:53.312140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:53.446791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:53.580541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:53.695831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:53.827209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:53.978301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:54.111896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:54.247760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:54.396919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:54.529550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:54.687857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:54.819719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:54.977149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:55.108708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:55.216605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:55.344824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:55.463171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:55.609335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:55.745512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:55.875309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:56.007806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:56.164193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:56.298324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:56.458925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:56.598552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:56.911708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:57.057295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:57.175011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:57.305662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:57.436427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:57.553272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:57.686918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:57.817563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:57.951209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:58.083854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:58.208523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:58.357127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:58.541651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:58.704184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:58.845838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:58.965124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:59.097910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:59.231491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:59.356189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:59.493821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:59.624475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T13:41:59.758115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-06-15T13:42:06.681535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-15T13:42:06.977749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-15T13:42:07.304037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-15T13:42:07.653344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-15T13:42:07.929828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-06-15T13:42:00.011438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-15T13:42:00.447273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-06-15T13:42:00.738537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-06-15T13:42:00.986871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexRequester external IDTicket created - DateChannelTicket IDAssignee emailRequester emailTicketsRequester_IDVolume_CHATVolume_EMAILVolume_SOCIALTempo_Medio_ChatTempo_Medio_EmailTempo_Medio_SocialAWT_Chat%NFCR%Insatisfação(CSAT)CSAT_Rated
0021593.02021-05-10Reclame Aqui3961927kaique.barbosa@gympass.comjoabinho99@hotmail.com121593NaN2.0NaNNaN13881.0NaNNaN0.5000000.00
1230084.02021-05-06Reclame Aqui3952516luciana.melo@gympass.comcamila.almeida@unilever.com130084NaN2.0NaNNaN9067.0NaNNaN0.6666670.00
2347027.02021-02-19Reclame Aqui3670243anderson.santos@gympass.comflashlav@gmail.com147027NaNNaNNaNNaNNaNNaNNaN0.0000000.00
3454194.02021-03-22Reclame Aqui3795980gabriel.almeida@gympass.comnancy.jikihara@voith.com1541941.01.0NaN1066.057865.0NaN169.00.5000001.01
4556234.02021-04-16Reclame Aqui3882995luciana.melo@gympass.comerikabazilio19@gmail.com1562341.0NaNNaN1100.0NaNNaN10.00.0000001.01
5757350.02021-04-01Reclame Aqui3831206luciana.melo@gympass.comtatiane.jesus@taesa.com.br1573502.0NaNNaN718.0NaNNaN53.00.0000000.00
6958058.02021-02-04Reclame Aqui3610902danielle.hernandes@gympass.comandreassenco@gmail.com1580581.0NaNNaN440.0NaNNaN37.00.0000000.00
71058246.02021-03-04Reclame Aqui3726711suellen.franco+core@gympass.comwasgoncalves@gmail.com158246NaNNaN1.0NaNNaN18179.0NaN0.0000000.00
81167449.02021-02-10Reclame Aqui3634981suellen.franco+core@gympass.combabixf@hotmail.com1674491.0NaNNaN392.0NaNNaN197.00.0000000.00
91281043.02021-05-12Reclame Aqui3972597danielle.hernandes@gympass.comdaniel.saggese@gmail.com181043NaN3.0NaNNaN6990.0NaNNaN0.3333331.01

Last rows

df_indexRequester external IDTicket created - DateChannelTicket IDAssignee emailRequester emailTicketsRequester_IDVolume_CHATVolume_EMAILVolume_SOCIALTempo_Medio_ChatTempo_Medio_EmailTempo_Medio_SocialAWT_Chat%NFCR%Insatisfação(CSAT)CSAT_Rated
66992018640265.02021-04-27Reclame Aqui3915302luciana.melo@gympass.comsneiva09@hotmail.com118640265NaN1.0NaNNaN3829.0NaNNaN0.500.000
67092218655961.02021-04-15Reclame Aqui3880457danielle.hernandes@gympass.comft.oportunidades@gmail.com1186559611.0NaNNaN3711.0NaNNaN19.00.001.001
67192318685162.02021-05-19Reclame Aqui3996381barbara.priscila@gympass.comthales.berdu@bayer.com1186851621.02.0NaN1013.012767.0NaN1691.00.501.001
67292418685771.02021-05-18Reclame Aqui3993205kaique.barbosa@gympass.comandre.martins@modal.com.br1186857715.01.0NaN962.011879.0NaN332.00.500.254
67392618814467.02021-05-24Reclame Aqui4012508barbara.priscila@gympass.comflaviaduarte@vooal.com1188144671.03.0NaN1333.06891.0NaN14.00.250.000
67492718838144.02021-05-04Reclame Aqui3942142kaique.barbosa@gympass.comhugovinicius93@hotmail.com1188381441.0NaNNaN702.0NaNNaN1750.00.001.001
67592818845344.02021-05-11Reclame Aqui3969697luciana.melo@gympass.comwendel.guedes@itau-unibanco.com.br1188453445.0NaNNaN1647.0NaNNaN24.01.001.002
67692918868795.02021-05-06Reclame Aqui3952988barbara.priscila@gympass.comrafael@vaozmedia.com.br118868795NaN1.0NaNNaN3787.0NaNNaN0.000.000
67793018934000.02021-05-14Reclame Aqui3982276barbara.priscila@gympass.comcontato.athleticpower@gmail.com118934000NaN7.0NaNNaN10299.0NaNNaN0.500.000
67893219066870.02021-01-07Reclame Aqui3489502suellen.franco+core@gympass.comlucashaddadf@gmail.com1190668701.01.0NaN1717.02931.0NaN38.00.000.000